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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Time-Series Graph00:54

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Relative Frequency Histogram01:14

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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Histogram01:05

Histogram

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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
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Scatter Plot01:15

Scatter Plot

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The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Forecasting Bitcoin Price Using Interval Graph and ANN Model: A Novel Approach.

R Murugesan1, V Shanmugaraja1, A Vadivel2

  • 1Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, Tamilnadu India.

SN Computer Science
|August 8, 2022
PubMed
Summary

This study introduces an Interval Graph Artificial Neural Network (IG-ANN) for Bitcoin price prediction. The novel IG-ANN model significantly outperforms traditional Artificial Neural Network (ANN) techniques.

Keywords:
ANN techniquesBitcoin priceForecastingIG-ANNInterval graphPrediction

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Area of Science:

  • * Computational Finance
  • * Artificial Intelligence
  • * Time Series Analysis

Background:

  • * Bitcoin price prediction is complex due to data volatility, non-linearity, and randomness.
  • * Accurate forecasting supports investment decisions and regulatory policy.
  • * Traditional methods struggle with the unique characteristics of Bitcoin time-series data.

Purpose of the Study:

  • * To develop an effective method for Bitcoin price forecasting.
  • * To address the challenges posed by Bitcoin's data characteristics.
  • * To introduce and evaluate the Interval Graph Artificial Neural Network (IG-ANN) model.

Main Methods:

  • * Transformation of raw Bitcoin time-series data using Interval Graphs (IG).
  • * Application of Artificial Neural Networks (ANN) on the transformed data.
  • * Windowing techniques capturing daily, weekly, and monthly price data.
  • * Performance evaluation using Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Dstat.

Main Results:

  • * The Interval Graph Artificial Neural Network (IG-ANN) demonstrated superior performance.
  • * IG-ANN significantly outperformed traditional Artificial Neural Network (ANN) models.
  • * Empirical studies on Bitcoin data from 2013-2019 confirmed IG-ANN's effectiveness.

Conclusions:

  • * The IG-ANN model offers a promising approach for accurate Bitcoin price prediction.
  • * Data transformation via Interval Graphs enhances the applicability of ANN models.
  • * This method provides a robust tool for financial forecasting and analysis.